问(Openmao2.4.0)在带导数的学科上不提供导数/强迫FD之间的区别

EN

import numpy as np

from openmdao.api import ExplicitComponent, Group, Problem, NewtonSolver, \
                         DirectSolver, IndepVarComp, ExecComp

class SellarDis1(ExplicitComponent):
    """
    Component containing Discipline 1 -- no derivatives version.
    """

    def setup(self):

        # Global Design Variable
        self.add_input('z', val=np.zeros(2))

        # Local Design Variable
        self.add_input('x', val=0.)

        # Coupling parameter
        self.add_input('y2', val=1.0)

        # Coupling output
        self.add_output('y1', val=1.0)

        # Finite difference all partials.
        # self.declare_partials('*', '*', method='fd')

    def compute(self, inputs, outputs):
        """
        Evaluates the equation
        y1 = z1**2 + z2 + x1 - 0.2*y2
        """
        z1 = inputs['z'][0]
        z2 = inputs['z'][1]
        x1 = inputs['x']
        y2 = inputs['y2']

        outputs['y1'] = z1**2 + z2 + x1 - 0.2*y2
        print('compute y1', outputs['y1'])

class SellarDis2(ExplicitComponent):
    """
    Component containing Discipline 2 -- no derivatives version.
    """

    def setup(self):
        # Global Design Variable
        self.add_input('z', val=np.zeros(2))

        # Coupling parameter
        self.add_input('y1', val=1.0)

        # Coupling output
        self.add_output('y2', val=1.0)

        # Finite difference all partials.
        # self.declare_partials('*', '*', method='fd')

    def compute(self, inputs, outputs):
        """
        Evaluates the equation
        y2 = y1**(.5) + z1 + z2
        """

        z1 = inputs['z'][0]
        z2 = inputs['z'][1]
        y1 = inputs['y1']
        print('y1', y1)

        # Note: this may cause some issues. However, y1 is constrained to be
        # above 3.16, so lets just let it converge, and the optimizer will
        # throw it out
        if y1.real < 0.0:
            y1 *= -1

        outputs['y2'] = y1**.5 + z1 + z2

class SellarMDA(Group):
    """
    Group containing the Sellar MDA.
    """

    def setup(self):
        indeps = self.add_subsystem('indeps', IndepVarComp(), promotes=['*'])
        indeps.add_output('x', 1.0)
        indeps.add_output('z', np.array([5.0, 2.0]))

        cycle = self.add_subsystem('cycle', Group(), promotes=['*'])
        cycle.add_subsystem('d1', SellarDis1(), promotes_inputs=['x', 'z', 'y2'], promotes_outputs=['y1'])
        cycle.add_subsystem('d2', SellarDis2(), promotes_inputs=['z', 'y1'], promotes_outputs=['y2'])

        # Nonlinear Block Gauss Seidel is a gradient free solver
        newton = cycle.nonlinear_solver = NewtonSolver()
        newton.options['iprint'] = 2
        newton.options['maxiter'] = 20
        newton.options['solve_subsystems'] = False
        cycle.linear_solver = DirectSolver()

        self.add_subsystem('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
                           z=np.array([0.0, 0.0]), x=0.0),
                           promotes=['x', 'z', 'y1', 'y2', 'obj'])

        self.add_subsystem('con_cmp1', ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
        self.add_subsystem('con_cmp2', ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])


prob = Problem()

prob.model = SellarMDA()

prob.setup()

prob['x'] = 2.
prob['z'] = [-1., -1.]

prob.run_model()


print(prob['y1'])
print(prob['y2'])